``` import xarray as xr import os # 定义输入目录和输出文件名 path = "\D:\桌面\污染物数据\CHAP_O3_D1K_2018_V2\第一年污染数据\" output_file = "annual_combined.nc" # 初始化一个空的数据集 combined_ds = None # 遍历目标路径下所有的 .nc 文件 for root, dirs, files in os.walk(path): for file in sorted(files): # 排序以确保时间顺序正确 if file.endswith(".nc"): file_path = os.path.join(root, file) try: ds = xr.open_dataset(file_path) # 打开当前文件 if combined_ds is None: combined_ds = ds # 如果是第一个文件，则初始化 combined_ds else: combined_ds = xr.concat([combined_ds, ds], dim='time') # 按 'time' 维度连接 except Exception as e: print(f"Error processing {file}: {e}") if combined_ds is not None: # 将合并后的数据集保存到新的 NetCDF 文件中 combined_ds.to_netcdf(output_file) print(f"Merged data saved to {output_file}") else: print("No valid .nc files found.")```在运行以上代码后发生错误：Cell In[19], line 5 path = "\D:\桌面\污染物数据\CHAP_O3_D1K_2018_V2\第一年污染数据\" ^ SyntaxError: unterminated string literal (detected at line 5) 如何解决

import xarray as xr import os # 定义输入目录和输出文件名 path = "D:\\桌面\\污染物数据\\CHAP_O3_D1K_2018_V2\\第一年污染数据\\" output_file = "annual_combined.nc" # 初始化一个空的数据集 combined_ds = None # 遍历目标路径下所有的 .nc 文件 for root, dirs, files in os.walk(path): for file in sorted(files): # 排序以确保时间顺序正确 if file.endswith(".nc"): file_path = os.path.join(root, file) try: ds = xr.open_dataset(file_path) # 打开当前文件 if combined_ds is None: combined_ds = ds # 如果是第一个文件，则初始化 combined_ds else: combined_ds = xr.concat([combined_ds, ds], dim='time') # 按 'time' 维度连接 except Exception as e: print(f"Error processing {file}: {e}") if combined_ds is not None: # 将合并后的数据集保存到新的 NetCDF 文件中 combined_ds.to_netcdf(output_file) print(f"Merged data saved to {output_file}") else: print("No valid .nc files found.")在运行以上代码后，返回：except Exception as e: print(f"Error processing {file}: {e}")

test_ds = xr.open_dataset(os.path.join(root, file)) print(f"{file} opened successfully.") except Exception as e: print(f"Failed to open {file}. Error: {str(e)}") 如果发现有些文件无法单独打开...

修改以下代码，虽然运行正常，但显示不正确，要求使两幅图的尺寸大致相等。import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, Normalize from matplotlib.gridspec import GridSpec import os import xarray as xr from scipy.stats import pearsonr from scipy.stats import spearmanr, kendalltau '''比较各个模型与标准模型（CSRM、GLDAS）的偏离程度，以标准线的形式给出''' def plot_model_comparisons(file_path: str, base_model: str = "CSRM", compare_models: list = ["RAPM", "TDAPM"],\ param_type: str = "trend", area_col: str = "area", save_path=r'D:\ill_pose\verify\outdata'): try: df = pd.read_excel(file_path) except FileNotFoundError: raise ValueError(f"文件不存在：{file_path}") # 验证参数有效性 valid_params = ["trend", "annual", 'semiannual'] if param_type not in valid_params: raise ValueError(f"参数类型必须为 {valid_params}") if param_type == 'trend': units = '(cm/yr)' else: units = '(cm)' # 自动生成列名模板 base_col = f"{param_type}-{base_model}" compare_cols = [f"{param_type}-{model}" for model in compare_models] # 检查列存在性 required_cols = [base_col, area_col] + compare_cols missing_cols = [col for col in required_cols if col not in df.columns] if missing_cols: raise KeyError(f"缺失必要列：{missing_cols}") # 创建子图布局 n_models = len(compare_models) ncols = min(3, n_models) # 每行最多3个子图 nrows = int(np.ceil(n_models / ncols)) fig = plt.figure(figsize=(12, 3 * nrows), dpi=500) gs = GridSpec(nrows, ncols, figure=fig) axes = [] # cmap = 'viridis' cmap = plt.get_cmap('RdYlBu_r') # 反转色带以符合常见地理可视化习惯 norm = Normalize(vmin=1e5, vmax=6e6) # norm = LogNorm(vmin=1e5, vmax=6e6) # 对数归一化 # 遍历每个对比模型 for i, model in enumerate(compare_models): # 创建子图 ax = fig.add_subplot(gs[i // ncols, i % ncols]) axes.append(ax) # 提取对比数据列 compare_col = f"{param_type}-{model}" df_plot = df[[base_col, compare_col, area_col]].copy()

import matplotlib.pyplot as plt fig, axs = plt.subplots(2, 2, figsize=(8, 6), # 统一画布尺寸 gridspec_kw={'width_ratios': [1, 1], # 宽度比例相同 'height_ratios': [1, 1]}) # 高度比例相同 plt.show...

import xarray as xr import pandas as pd from pathlib import Path import os def load_and_process_data(dir_path, var_name="ozone"): # Step 1 - Check if path is valid and has any '.nc' files. data_directory = Path(dir_path) if not data_directory.is_dir(): raise NotADirectoryError(f"The provided path {dir_path} doesn't point to a valid folder.") nc_files = [str(f) for f in sorted(data_directory.glob(".nc"))] if len(nc_files)==0 : raise FileNotFoundError(f"No '.nc' files were found inside the given location:{dir_path}") try: ds = xr.open_mfdataset(nc_files, combine='by_coords') except Exception as e: return str(e)+"\nFailed loading dataset" df=ds[var_name].to_dataframe().reset_index() # Convert time column into proper datetime format then set it up properly within index system again before doing resampling work later on.. df['time']=pd.to_datetime(df['time']) ; df.set_index('time',inplace=True) monthly_avg=df.resample('MS').mean() final_ds=(monthly_avg.reset_index()).set_index(['time']).to_xarray() output_file=f'{var_name}_monthly_average_output.nc' final_ds.to_netcdf(output_file) return "Successfully processed & saved!" try: result_message=load_and_process_data('./第一年污染数据/',"ozone") except BaseException as errormsg: print(errormsg) # If everything runs fine until here,you'll see success message printed out at terminal side! print(result_message)在运行以上代码后，提示：[Errno 2] No such file or directory: 'C:\\Users\\Administrator\\第一年污染数据\\CHAP_O3_D1K_20180101_V2.nc' Failed loading dataset 是什么意思，如何解决

从错误信息 [Errno 2] No such file or directory 可以看出，程序在尝试访问指定路径 ./第一年污染数据/ 下的一个 .nc 文件时失败了，原因是该文件不存在或路径无效。 ### 错误分析： 1. **路径问题**： ...

要求结合地面气压计算整层水汽通量：import numpy as np import xarray as xr import cartopy.crs as ccrs import cartopy.feature as cfeature import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap, BoundaryNorm from cartopy.io import shapereader from cartopy.feature import NaturalEarthFeature from matplotlib.patches import Rectangle # ==== 数据预处理 ==== # 读取数据 f_mos = xr.open_dataset('/mnt/d/fuxian/shum.mon.mean.nc') # 比湿 (单位: g/kg) f_u1 = xr.open_dataset('/mnt/d/fuxian/uwnd.mon.mean.nc') # 纬向风 (m/s) f_v1 = xr.open_dataset('/mnt/d/fuxian/vwnd.mon.mean.nc') # 经向风 (m/s) f_pre = xr.open_dataset('/mnt/d/fuxian/CN05.1_Pre_1961-2022_month_025x025.nc') # 读取地面气压数据 f_pres = xr.open_dataset('/mnt/d/fuxian/pres.sfc.mon.mean.nc') # 地面气压 (单位: Pa) # 时间筛选（1970-2015年6-7月） def filter_jj(data): return data.sel(time=slice('1970-01-01', '2015-12-01')) \ .where((data['time.month'] == 6) | (data['time.month'] == 7), drop=True) jj_mos = filter_jj(f_mos) jj_u = filter_jj(f_u1) jj_v = filter_jj(f_v1) jj_pre = filter_jj(f_pre) jj_pres = filter_jj(f_pres) # ==== 气候平均计算 ==== # 转换为kg/kg并计算平均 shum_mean = jj_mos['shum'].mean(dim='time') / 1000 # g/kg → kg/kg u_mean = jj_u['uwnd'].mean(dim='time') v_mean = jj_v['vwnd'].mean(dim='time') pre_mean = jj_pre['pre'].mean(dim='time') pres_mean = jj_pres['pres'].mean(dim='time') # 地面气压平均 lon1 = pre_mean['lon'].values lat1 = pre_mean['lat'].values # 获取经纬度信息 lon = shum_mean['lon'].values lat = shum_mean['lat'].values

好的，我现在需要帮助用户解决如何使用Python中的numpy、xarray、cartopy和matplotlib结合地面气压数据计算整层水汽通量并绘制地图的问题。首先，我得理解用户的具体需求。用户提到他们有一个.nc格式的地形文件，...

import xarray as xr import matplotlib.pyplot as plt if name=='main': ds=xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst=ds['sst'] sstsel=sst.loc[:,5:-5,190:240] #5°S-5°N,170°W-120°W enso=sstsel.mean(dim=['lat', 'lon'])#区域平均修改这段代码，计算enso的距平

import xarray as xr import matplotlib.pyplot as plt if __name__=='__main__': ds=xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst=ds['sst'] sstsel=sst.loc[:,5:-5,190:240] #5°S-5°N,170°W-...

import pandas as pd import xarray as xr from tqdm import tqdm from dask.diagnostics import ProgressBar import numpy as np # 读取NetCDF文件 file_path = 'D:/thesis of 2025/Data/Database/original/2020-2024windspeed.nc' output_path = 'D:/thesis of 2025/Data/Database/modification/windspeed_2020-2024.csv' ds = xr.open_dataset(file_path,chunks={'valid_time': 1624}) # 分块读取数据 # 提取100米风速的u和v分量 u100 = ds['u100'] # 提取100米风速的u分量 v100 = ds['v100'] # 提取100米风速的v分量 # 初始化进度条 with ProgressBar(): # 将u100风速数据转换为二维数组 u100_2d_0 = u100.stack(latlon=('latitude', 'longitude')).to_pandas() # 重置索引，将时间作为一维，合并后的经度和纬度作为另一维 u100_2d = u100_2d_0.reset_index() with ProgressBar(): # 将v100风速数据转换为二维数组 v100_2d_0 = v100.stack(latlon=('latitude', 'longitude')).to_pandas() # 重置索引，将时间作为一维，合并后的经度和纬度作为另一维 v100_2d = v100_2d_0.reset_index() # 计算实际风速（根据勾股定理） with ProgressBar(): windspeed_2d = pd.concat([u100_2d, v100_2d], axis=1) windspeed_2d['windspeed'] = np.sqrt(windspeed_2d['u100']2 + windspeed_2d['v100']2) # 保存到CSV文件 with ProgressBar(): windspeed_2d.to_csv(output_path, index=True) # 储存文件 # 使用pandas读取CSV文件的前5行 df = pd.read_csv(output_path, nrows=5) print(df) 在后两个过程（计算实际风速、保存文件）中，会显示进度条吗？如不会，该如何显示进度条

嗯，用户想知道在使用Dask和Pandas处理数据时如何正确显示进度条，特别是在计算实际风速和保存文件的时候。首先，我需要回忆一下Dask和Pandas在处理进度条方面的不同机制。用户提到了Dask的ProgressBar，我记得...

import xarray as xr import matplotlib.pyplot as plt from netCDF4 import Dataset import cartopy.crs as ccrs#投影方式 import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import numpy as np import pandas as pd from matplotlib.font_manager import fontManager da=pd.read_csv(r'E:\python11\STATION_58237.txt',sep='\s+') print(da) tem=da['TEM'] #print(tem.shape) rhu=da['RHU'] tem_ave=[] for i in range(0,24): tem_data=tem[i:147:24] print(tem_data) tem_ave[i]=np.mean(tem_data) print(tem_ave) plt.plot(tem_ave)帮我改一下

import xarray as xr import matplotlib.pyplot as plt from netCDF4 import Dataset import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import ...

分析代码import xarray as xr

这段代码是在Python中导入xarray模块，xarray是一个Python库，用于处理标签多维数组的数据结构。它提供了一种高效的方法来处理和分析具有不同维度和标签的数据。这个库可以很好地处理气象、海洋学、气候和地球科学等...

start_time = time.time() othercon = 'Profile_Time >= "{}" and Profile_Time <"{}" and high_level > 338'.format(desday,tom_dt.strftime('%Y-%m-%d')) # apro_df 是[latitude,longitude,time,high_level，features]的格式，但是高度还没有std apro_ori, apro_df, apro_xr = get_apro_data_sql(con, apro_config, othercon, pos_merge=pos_df, multi_index=multi_index + ['high_level']) print('THE COST to get raw data table:',time.strftime("%H: %M: %S",time.gmtime(time.time() - start_time))) # TODO: 可能查不到数据，判断一下 if apro_df.shape[0] == 0: # 修改列名即可 apro_final_df = apro_df apro_final_df.rename(columns={'high_level':'Level'},inplace=True) print('THE {} DAY HAS NO APRO DATA'.format(desday)) else: # 高度标准化 apro_df['Level'] = apro_df.apply(apro_get_level, axis=1) apro_df = apro_df.drop(['high_level'], axis=1) apro_xr = apro_df.set_index(['Time', 'Latitude', 'Longitude', 'Level']).to_xarray() # 插值等 # 2. 插值 _, _, times, tlabels = get_apro_interp_attr(apro_xr, std_index_3d, desday,posrange) # 时间 apro_mean_xr = apro_xr.groupby_bins('Time', bins=times, labels=tlabels).mean('Time').rename( {'Time_bins': 'Time'}) # 位置 apro_mean_xr['Latitude'] = apro_mean_xr.Latitude.values.round(1) apro_mean_xr['Longitude'] = apro_mean_xr.Longitude.values.round(1) apro_mean_df = apro_mean_xr.to_dataframe().dropna(how='all').reset_index() # 最后 apro_final_df = apro_mean_df.groupby(['Time', 'Latitude', 'Longitude', 'Level']).mean().dropna(how='all') # apro_final_xr = apro_final_df.to_xarray() apro_final_df = apro_final_df.reset_index() # 修改时间 apro_final_df.Time = pd.to_datetime(apro_final_df['Time']) apro_final_df.Time = apro_final_df['Time'].apply(lambda x:x.replace(year=2023)) # Todo: 可以改成输入的年份 # 输出中间文件,可能是空文件 desday = desday.replace('2017','2023') outfile = os.path.join(apro_config.outpath,"apro_mid_{}.csv".format(desday)) apro_final_df.to_csv(outfile,index=False)

这段代码执行了一系列处理apro_df数据的步骤。让我逐一解释一下：首先，代码使用time.time()获取当前时间，并将其保存在start_time变量中。然后，根据desday和tom_dt生成一个查询条件othercon。接下来，代码调用...

import xarray as xr import pandas as pd # 批量加载整个年度的所有 nc 数据文件 ds = xr.open_mfdataset('D:\桌面\污染物数据\CHAP_O3_D1K_2018_V2\第一年污染数据/*.nc', combine='by_coords') #将时间维度转换为便于分组的时间序列对象以便后续聚合操作。这里假设变量名为ozone表示臭氧浓度。 df = ds['ozone'].to_dataframe().reset_index() #使用pd.to_datetime()将时间列转化为标准 datetime 类型，并设置其作为索引。接着调用resample()方法完成按月份的重新采样过程。 df['time'] = pd.to_datetime(df['time']) df.set_index('time', inplace=True) monthly_mean = df.resample('M').mean() # 计算每个月份均值 #最后一步就是把计算得到的结果保存回一个新的 .nc 文件当中去。 monthly_ds = monthly_mean.reset_index().set_index(['time']).to_xarray() monthly_ds.to_netcdf('output_monthly_ozone.nc')在运行以上代码后出现错误：KeyError: [<class 'netCDF4._netCDF4.Dataset'>, ('D:\\桌面\\污染物数据\\CHAP_O3_D1K_2018_V2\\第一年污染数据\\CHAP_O3_D1K_20180101_V2.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '2831862b-530c-4693-8b7f-8fb555ba8f64'] During handling of the above exception, another exception occurred:

ds = xr.open_mfdataset(r'D:\桌面\污染物数据\CHAP_O3_D1K_2018_V2\第一年污染数据/*.nc', combine='by_coords') print(ds.variables.keys()) 上述命令将会列出所有的可用变量名。然后根据实际存在的变量替换掉...

import xarray as xr import numpy as np import matplotlib.pyplot as plt import cartopy.crs as ccrs def readdat(pth,var): ds = xr.open_dataset(pth) dat = ds[var] # 只读取每年1月的数据 dat = dat.sel(time=dat['time.month'].isin([1])) dat = dat.loc['1981-01-01':'2018-12-01','500',:,:] return dat if name=='main': pth=r'D:\6termsx\qhyc\sx1\hgt.mon.mean.nc' hgt=readdat(pth,'hgt') #print(hgt.shape) #print(hgt) wp = np.zeros(38) # 初始化空数组 for i in range(38): wp[i] =0.5*(hgt[i,12,62]-hgt[i,24,62]) wpm=np.mean(wp) #平均 wpa=wp[:]-np.tile(wpm,(wp.shape))#中心化 #print(wpa.shape) wps=wpa/np.tile(np.std(wpa),(wpa.shape))#标准化 #print(wps) x = np.linspace(1981,2018,38) plt.plot(x,wps) plt.show()最后如何将图像保存为PDF

import xarray as xr import numpy as np import matplotlib.pyplot as plt import cartopy.crs as ccrs def readdat(pth,var): ds = xr.open_dataset(pth) dat = ds[var] # 只读取每年1月的数据 dat = dat.sel...

import xarray as xr import numpy as np from scipy.stats import gamma from scipy.special import erfinv import warnings warnings.filterwarnings('ignore') #%% 基础配置 input_path = r'D:\heatwave\data\precip\precip_merged.nc' output_dir = r'D:\heatwave\data\precip' lat_range = [15, 55] lon_range = [105, 140] #%% 步骤1：增强型数据预处理 def preprocess_data(): ds = xr.open_dataset(input_path) # 统一维度命名 rename_dict = {} if 'latitude' in ds.dims: rename_dict['latitude'] = 'lat' if 'longitude' in ds.dims: rename_dict['longitude'] = 'lon' ds = ds.rename(rename_dict) # 空间裁剪 ds_china = ds.sel(lat=slice(lat_range), lon=slice(lon_range)) # 移除闰日 ds_china = ds_china.sel(time=~((ds_china['time.month'] == 2) & (ds_china['time.day'] == 29))) return ds_china #%% 步骤2：修正后的SPI-90计算（关键修改） def calculate_spi90(precip): # 90天累积降水 precip_roll = precip.rolling(time=90, min_periods=90, center=False).sum() # 改进Gamma拟合函数 def gamma_fit(data): valid_data = data[~np.isnan(data)] if len(valid_data) < 5 or np.all(valid_data == 0): return np.array([np.nan, 0, 0]) try: a, loc, scale = gamma.fit(valid_data + 1e-6, floc=0) return np.array([a, loc, scale]) except: return np.array([np.nan, 0, 0]) # 关键修改：使用兼容性覆盖合并 grouped = precip_roll.groupby('time.dayofyear') params = grouped.map( lambda x: xr.DataArray(gamma_fit(x.values), dims=['param'], coords={'param': ['a', 'loc', 'scale']}) ) # 创建参数数据集（修复合并冲突） a = params.sel(param='a').rename('a') scale = params.sel(param='scale').rename('scale') params_ds = xr.merge([a, scale], compat='override') # 关键修复 params_ds = params_ds.assign_coords(dayofyear=params.dayofyear) # 核心计算逻辑优化 def calculate_gamma_cdf(data, doy): a = params_ds['a'].sel(dayofyear=doy).values scale = params_ds['scale'].sel(dayofyear=doy).values return gamma.cdf(data, a=a, loc=0, scale=scale) # 应用计算 spi90 = xr.apply_ufunc( calculate_gamma_cdf, precip_roll, precip_roll['time.dayofyear'], input_core_dims=[[], []], output_core_dims=[[]], vectorize=True, dask='parallelized', output_dtypes=[float] ) # 转换为标准正态分布 spi90 = xr.apply_ufunc( lambda x: np.sqrt(2)erfinv(2x-1) if not np.isnan(x) else np.nan, spi90, dask='parallelized', output_dtypes=[float] ) return spi90.rename('SPI90').transpose('time', 'lat', 'lon') #%% 步骤3：主处理流程 def main_process(): ds = preprocess_data() spi90 = calculate_spi90(ds['precip']) # 编码设置 encoding = { 'SPI90': {'zlib': True, 'complevel': 1, 'dtype': 'float32'}, 'lat': {'dtype': 'float32'}, 'lon': {'dtype': 'float32'}, 'time': {'dtype': 'int32', 'units': 'days since 1900-01-01'} } # 输出文件 spi90.to_netcdf( f'{output_dir}/SPI90_daily.nc', encoding=encoding, unlimited_dims=['time'] ) print(f"处理完成！维度验证：{spi90.dims}") #%% 执行 if name == 'main': main_process()这段代码得到的SPI90虽然有lat这个维度但是这个维度为0，即（lat: 0，lon: 70，time: 16060）帮我解决这个问题，并且重新提供完整的代码

import xarray as xr import numpy as np from scipy.stats import gamma from scipy.special import erfinv import warnings warnings.filterwarnings('ignore') #%% 基础配置（保持不变） input_path = r'D:\...

import xarray as xr import pandas as pd import cftime ds = xr.open_dataset('/Users/guodongjie/Documents/科研/data/Temp/2016-2023.nc')

看起来您正在使用xarray库打开一个名为'2016-2023.nc'的NetCDF文件。请继续，我会尽力帮助您解决任何问题。请注意，要成功打开NetCDF文件，您需要确保已经安装了相关的依赖项，如netcdf4、h5py等。如果您尚未安装...

优化这个代码import xarray as xr import netCDF4 as nc import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt import cartopy.mpl.ticker as cticker import cartopy.crs as ccrs import cartopy.feature as cfeature ds = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc', engine='netcdf4') # 读取原始数据 ds_temp = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc') # 区域提取* south_asia = ds_temp.sel(latitude=slice(38, 28), longitude=slice(75, 103)) indian_ocean = ds_temp.sel(latitude=slice(5, -15), longitude=slice(60, 100)) # 高度插值 south_asia_200hpa = south_asia.t.interp(level=200) indian_ocean_200hpa = indian_ocean.t.interp(level=200) south_asia_400hpa = south_asia.t.interp(level=400) indian_ocean_400hpa = indian_ocean.t.interp(level=400) # 区域平均 TTP = south_asia_400hpa.mean(dim=('latitude', 'longitude'))#.values TTIO = indian_ocean_400hpa.mean(dim=('latitude', 'longitude'))# TTP_200hpa = south_asia_200hpa.mean(dim=('latitude', 'longitude')) TTIO_200hpa = indian_ocean_200hpa.mean(dim=('latitude', 'longitude')) tlup=(TTP-TTIO)-(TTP_200hpa-TTIO_200hpa)-(-5.367655815) # 定义画图区域和投影方式 fig = plt.figure(figsize=[10, 8]) ax = plt.axes(projection=ccrs.PlateCarree()) # 添加地图特征 ax.set_extent([60, 140, -15, 60], crs=ccrs.PlateCarree()) ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidths=0.5) ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='lightgray') ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor='white') # 画距平场 im = ax.contourf(TTP_200hpa, TTP, tlup, cmap='coolwarm', levels=np.arange(-4, 4.5, 0.5), extend='both') # 添加色标 cbar = plt.colorbar(im, ax=ax, shrink=0.8) cbar.set_label('Temperature anomaly (°C)') # 添加经纬度坐标轴标签 ax.set_xticks(np.arange(60, 105, 10), crs=ccrs.PlateCarree()) ax.set_yticks(np.arange(-10, 40, 10), crs=ccrs.PlateCarree()) lon_formatter = cticker.LongitudeFormatter() lat_formatter = cticker.LatitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) ax.yaxis.set_major_formatter(lat_formatter) # 添加标题和保存图片 plt.title('Temperature anomaly at 400hPa over South Asia and the Indian Ocean') plt.savefig('temperature_anomaly.png', dpi=300) plt.show()

import xarray as xr, netCDF4 as nc, pandas as pd, numpy as np, datetime import matplotlib.pyplot as plt import cartopy.mpl.ticker as cticker, cartopy.crs as ccrs, cartopy.feature as cfeature 2. ...

import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from datetime import datetime, timedelta # 1. 数据加载（修改后的版本） def load_data(u_file, v_file, hgt_file): # 分别读取三个文件 ds_u = xr.open_dataset(u_file).sel(level=200) # 选择200hPa层 ds_v = xr.open_dataset(v_file).sel(level=200) ds_hgt = xr.open_dataset(hgt_file).sel(level=200) # 合并数据集（确保时间维度对齐） ds = xr.merge([ds_u.rename({'u': 'u200'}), # 防止变量名冲突 ds_v.rename({'v': 'v200'}), ds_hgt.rename({'hgt': 'hgt200'})]) # 统一处理时间坐标（如果时间格式不一致） if 'time' in ds.coords: ds['time'] = xr.decode_cf(ds).time return ds # 2. 修改后的绘图函数 def plot_weather(ds, time): # 筛选指定时间的数据 ds_time = ds.sel(time=time, method='nearest') # 处理时间对齐 # 创建地图底图 fig, ax = create_map() # 绘制高度场等值线（变量名改为hgt200） levels = range(int(ds.hgt200.min()), int(ds.hgt200.max()), 40) cont = ax.contour(ds.lon, ds.lat, ds_time.hgt200, # 注意已无level维度 levels=levels, colors='black', linewidths=1.5, transform=ccrs.PlateCarree()) plt.clabel(cont, inline=True, fontsize=10, fmt='%d') # 绘制风场（使用u200和v200） wind_slice = slice(None, None, 2) # 每2个点取一个箭头 quiver = ax.quiver(ds.lon[wind_slice], ds.lat[wind_slice], ds_time.u200[wind_slice,wind_slice], ds_time.v200[wind_slice,wind_slice], scale=500, color='red', transform=ccrs.PlateCarree()) # 添加标题和比例尺（保持不变） plt.title(f"200hPa Weather Situation\n{time.strftime('%Y-%m-%d %H:%M')}", fontsize=14, pad=20) ax.quiverkey(quiver, X=0.85, Y=0.1, U=50, label='50 m/s', labelpos='E') plt.savefig(f"weather_{time.strftime('%Y%m%d%H')}.png", dpi=300) plt.close() # 3. 主程序调整 if name == "main": # 加载数据（注意新参数顺序） ds = load_data('u_wind.nc', 'v_wind.nc', 'hgt_data.nc') # 确保数据按时间排序（重要！） ds = ds.sortby('time') # 生成时间序列（保持不变） current_time = start_time while current_time <= end_time: print(f"Processing: {current_time}") plot_weather(ds, current_time) current_time += time_step

import xarray as xr # 处理NetCDF格式气象数据 import matplotlib.pyplot as plt # 绘图 import cartopy.crs as ccrs # 地图投影 import cartopy.feature as cfeature # 地理特征 from datetime import datetime, ...

import xarray as xr import matplotlib.pyplot as plt if name=='main': ds=xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst=ds['sst'] sstsel=sst.loc[:,5:-5,190:240] #5°S-5°N,170°W-120°W enso=sstsel.mean(dim=['lat', 'lon'])#区域平均 ensom=enso.mean('time') #print(ensom) ensoa=enso-ensom #print(ensoa)修改这段代码，筛选出大于0.5的值

import xarray as xr import matplotlib.pyplot as plt if __name__ == '__main__': ds = xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst = ds['sst'] sstsel = sst.loc[:, 5:-5, 190:240] # 5°S-5...

电子商务和网络营销的概念区别(1).docx

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import xarray as xr import matplotlib.pyplot as plt if __name__=='__main__': ds=xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst=ds['sst'] sstsel=sst.loc[:,5:-5,190:240] #5°S-5°N,170°W-120°W enso=sstsel.mean(dim=['lat', 'lon'])#区域平均修改这段代码，计算enso的距平

分析代码import xarray as xr

import xarray as xr import pandas as pd import cftime ds = xr.open_dataset('/Users/guodongjie/Documents/科研/data/Temp/2016-2023.nc')

电子商务和网络营销的概念区别(1).docx

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import xarray as xr import matplotlib.pyplot as plt if name=='main': ds=xr.open_dataset(r'D:\pythonsx\zl\sst.mnmean.nc') sst=ds['sst'] sstsel=sst.loc[:,5:-5,190:240] #5°S-5°N,170°W-120°W enso=sstsel.mean(dim=['lat', 'lon'])#区域平均修改这段代码，计算enso的距平